


import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


__UpperCamelCase     		=							'bert-base-cased'
__UpperCamelCase     		=							'google/pegasus-xsum'
__UpperCamelCase     		=							[' Sam ate lunch today.', 'Sams lunch ingredients.']
__UpperCamelCase     		=							['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
__UpperCamelCase     		=							'patrickvonplaten/t5-tiny-random'
__UpperCamelCase     		=							'sshleifer/bart-tiny-random'
__UpperCamelCase     		=							'sshleifer/tiny-mbart'
__UpperCamelCase     		=							'sshleifer/tiny-marian-en-de'
def 							UpperCamelCase_(					_A :Path					,  _A :list	)->		Any:
			UpperCamelCase__       =							"\n".join(_A	)
			Path(_A	).open("w"	).writelines(_A	)
def 							UpperCamelCase_(					_A :Optional[int]	)->		int:
			for split in ["train", "val", "test"]:
						_dump_articles(os.path.join(_A					,  F'''{split}.source'''	)					,  _A	)
						_dump_articles(os.path.join(_A					,  F'''{split}.target'''	)					,  _A	)
			return tmp_dir







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			@parameterized.expand(
			    [
			        MBART_TINY,
			        MARIAN_TINY,
			        T5_TINY,
			        BART_TINY,
			        PEGASUS_XSUM,
			    ]   ,    )
			@slow
			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							AutoTokenizer.from_pretrained(snake_case  )
						UpperCamelCase__       =							make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
						UpperCamelCase__       =							max(len(tokenizer.encode(snake_case  )  ) for a in ARTICLES  )
						UpperCamelCase__       =							max(len(tokenizer.encode(snake_case  )  ) for a in SUMMARIES  )
						UpperCamelCase__       =							4
						UpperCamelCase__       =							8
						assert max_len_target > max_src_len  # Will be truncated
						assert max_len_source > max_src_len  # Will be truncated
						UpperCamelCase__,   UpperCamelCase__       =							"ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
						UpperCamelCase__       =							SeqaSeqDataset(
						    snake_case   ,    data_dir=snake_case   ,    type_path="train"   ,    max_source_length=snake_case   ,    max_target_length=snake_case   ,    src_lang=snake_case   ,    tgt_lang=snake_case   ,    )
						UpperCamelCase__       =							DataLoader(snake_case   ,    batch_size=2   ,    collate_fn=train_dataset.collate_fn  )
						for batch in dataloader:
									assert isinstance(snake_case   ,    snake_case  )
									assert batch["attention_mask"].shape == batch["input_ids"].shape
									# show that articles were trimmed.
									assert batch["input_ids"].shape[1] == max_src_len
									# show that targets are the same len
									assert batch["labels"].shape[1] == max_tgt_len
									if tok_name != MBART_TINY:
												continue
									# check language codes in correct place
									UpperCamelCase__       =							shift_tokens_right(batch["labels"]   ,    tokenizer.pad_token_id  )
									assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
									assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
									assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
									assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

									break  # No need to test every batch




			@parameterized.expand([BART_TINY, BERT_BASE_CASED]  )
			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							AutoTokenizer.from_pretrained(snake_case  )
						UpperCamelCase__       =							make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
						UpperCamelCase__       =							max(len(tokenizer.encode(snake_case  )  ) for a in ARTICLES  )
						UpperCamelCase__       =							max(len(tokenizer.encode(snake_case  )  ) for a in SUMMARIES  )
						UpperCamelCase__       =							4
						UpperCamelCase__       =							LegacySeqaSeqDataset(
						    snake_case   ,    data_dir=snake_case   ,    type_path="train"   ,    max_source_length=20   ,    max_target_length=snake_case   ,    )
						UpperCamelCase__       =							DataLoader(snake_case   ,    batch_size=2   ,    collate_fn=train_dataset.collate_fn  )
						for batch in dataloader:
									assert batch["attention_mask"].shape == batch["input_ids"].shape
									# show that articles were trimmed.
									assert batch["input_ids"].shape[1] == max_len_source
									assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
									# show that targets were truncated
									assert batch["labels"].shape[1] == trunc_target  # Truncated
									assert max_len_target > trunc_target  # Truncated
									break  # No need to test every batch




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"  )

						UpperCamelCase__       =							Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )  )
						UpperCamelCase__       =							tmp_dir.joinpath("train.source"  ).open().readlines()
						UpperCamelCase__       =							Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )  )
						pack_data_dir(snake_case   ,    snake_case   ,    128   ,    snake_case  )
						UpperCamelCase__       =							{x.name for x in tmp_dir.iterdir()}
						UpperCamelCase__       =							{x.name for x in save_dir.iterdir()}
						UpperCamelCase__       =							save_dir.joinpath("train.source"  ).open().readlines()
						# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
						# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
						assert len(snake_case  ) < len(snake_case  )
						assert len(snake_case  ) == 1
						assert len(packed_examples[0]  ) == sum(len(snake_case  ) for x in orig_examples  )
						assert orig_paths == new_paths




			@pytest.mark.skipif(not FAIRSEQ_AVAILABLE   ,    reason="This test requires fairseq"  )
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						if not FAIRSEQ_AVAILABLE:
									return
						UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__       =							self._get_dataset(max_len=64  )
						UpperCamelCase__       =							64
						UpperCamelCase__       =							ds.make_dynamic_sampler(snake_case   ,    required_batch_size_multiple=snake_case  )
						UpperCamelCase__       =							[len(snake_case  ) for x in batch_sampler]
						assert len(set(snake_case  )  ) > 1  # it's not dynamic batch size if every batch is the same length
						assert sum(snake_case  ) == len(snake_case  )  # no dropped or added examples
						UpperCamelCase__       =							DataLoader(snake_case   ,    batch_sampler=snake_case   ,    collate_fn=ds.collate_fn   ,    num_workers=2  )
						UpperCamelCase__       =							[]
						UpperCamelCase__       =							[]
						for batch in data_loader:
									UpperCamelCase__       =							batch["input_ids"].shape
									UpperCamelCase__       =							src_shape[0]
									assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
									UpperCamelCase__       =							np.product(batch["input_ids"].shape  )
									num_src_per_batch.append(snake_case  )
									if num_src_tokens > (max_tokens * 1.1):
												failures.append(snake_case  )
						assert num_src_per_batch[0] == max(snake_case  )
						if failures:
									raise AssertionError(F'''too many tokens in {len(snake_case  )} batches'''  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__       =							self._get_dataset(max_len=512  )
						UpperCamelCase__       =							2
						UpperCamelCase__       =							ds.make_sortish_sampler(snake_case   ,    shuffle=snake_case  )

						UpperCamelCase__       =							DataLoader(snake_case   ,    batch_size=snake_case   ,    collate_fn=ds.collate_fn   ,    num_workers=2  )
						UpperCamelCase__       =							DataLoader(snake_case   ,    batch_size=snake_case   ,    collate_fn=ds.collate_fn   ,    num_workers=2   ,    sampler=snake_case  )

						UpperCamelCase__       =							tokenizer.pad_token_id

						def count_pad_tokens(snake_case   ,    snake_case="input_ids"  ):
									return [batch[k].eq(snake_case  ).sum().item() for batch in data_loader]

						assert sum(count_pad_tokens(snake_case   ,    k="labels"  )  ) < sum(count_pad_tokens(snake_case   ,    k="labels"  )  )
						assert sum(count_pad_tokens(snake_case  )  ) < sum(count_pad_tokens(snake_case  )  )
						assert len(snake_case  ) == len(snake_case  )




			def 					snake_case__					(				self   ,    snake_case=1000   ,    snake_case=128  ):

						'''simple docstring'''


						if os.getenv("USE_REAL_DATA"   ,    snake_case  ):
									UpperCamelCase__       =							"examples/seq2seq/wmt_en_ro"
									UpperCamelCase__       =							max_len * 2 * 64
									if not Path(snake_case  ).joinpath("train.len"  ).exists():
												save_len_file(snake_case   ,    snake_case  )
						else:
									UpperCamelCase__       =							"examples/seq2seq/test_data/wmt_en_ro"
									UpperCamelCase__       =							max_len * 4
									save_len_file(snake_case   ,    snake_case  )

						UpperCamelCase__       =							AutoTokenizer.from_pretrained(snake_case  )
						UpperCamelCase__       =							SeqaSeqDataset(
						    snake_case   ,    data_dir=snake_case   ,    type_path="train"   ,    max_source_length=snake_case   ,    max_target_length=snake_case   ,    n_obs=snake_case   ,    )
						return ds, max_tokens, tokenizer




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__       =							self._get_dataset()
						UpperCamelCase__       =							set(DistributedSortishSampler(snake_case   ,    256   ,    num_replicas=2   ,    rank=0   ,    add_extra_examples=snake_case  )  )
						UpperCamelCase__       =							set(DistributedSortishSampler(snake_case   ,    256   ,    num_replicas=2   ,    rank=1   ,    add_extra_examples=snake_case  )  )
						assert idsa.intersection(snake_case  ) == set()







			@parameterized.expand(
			    [
			        MBART_TINY,
			        MARIAN_TINY,
			        T5_TINY,
			        BART_TINY,
			        PEGASUS_XSUM,
			    ]   ,    )
			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							AutoTokenizer.from_pretrained(snake_case   ,    use_fast=snake_case  )
						if tok_name == MBART_TINY:
									UpperCamelCase__       =							SeqaSeqDataset(
									    snake_case   ,    data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )   ,    type_path="train"   ,    max_source_length=4   ,    max_target_length=8   ,    src_lang="EN"   ,    tgt_lang="FR"   ,    )
									UpperCamelCase__       =							train_dataset.dataset_kwargs
									assert "src_lang" in kwargs and "tgt_lang" in kwargs
						else:
									UpperCamelCase__       =							SeqaSeqDataset(
									    snake_case   ,    data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )   ,    type_path="train"   ,    max_source_length=4   ,    max_target_length=8   ,    )
									UpperCamelCase__       =							train_dataset.dataset_kwargs
									assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
									assert len(snake_case  ) == 1 if tok_name == BART_TINY else len(snake_case  ) == 0






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
						from .pipeline_kandinsky import KandinskyPipeline
						from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
						from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
						from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
						from .text_encoder import MultilingualCLIP


